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In the last few years, we have seen an increase in the number of mobile applications and their usage. The architectural pattern and correctness represent a major factor in the success of a mobile project and its lifecycle. Since architectural patterns can not be imposed on a codebase without an external validation system, we are proposing a novel technique for automatically analysing one of the most commonly used presentational architectural pattern Model-View-Controller (MVC). Our system analyses and detects architectural issues by leveraging information obtain from Software Development Kits (SDKs). For the evaluation of our proposal, we have focused on mobile codebases (private and open source), most specifically on the iOS platform. However, our findings can be extrapolated to other platforms which use SDKs for building their user interfaces (UI). [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. Approach: Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs' performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. Results: We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. Conclusions: The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points. [ABSTRACT FROM AUTHOR]

Since computer processing mainly depends on sorting and searching methods, a key problem is how to design efficient algorithms in order to solve such problems. This paper describes a new nature-inspired mechanism (called Friction-based Sorting) capable of sorting a given set of numbers. The main idea behind this mechanism is to associate a ball (whose weight is proportional to the considered number) to each number. All the balls being allowed to fall in the presence of friction, the heaviest ball (which corresponds to the greatest input number) will reach the ground first and the lightest ball (associated with the smallest number) will reach the ground last. The proposed mechanism is analyzed, together with its strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels - also known as a multiple kernel (MK) - in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK - linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance. [ABSTRACT FROM AUTHOR]

A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions. [ABSTRACT FROM AUTHOR]

Complex Networks theory represents a powerful tool to model real-world systems as graphs with non-trivial topological features. Static by their definition, complex networks are limited to be the reflection or the snapshot of the dynamical systems they encode in a given moment. Frankly, studies show that the network preserves the characteristics of the dynamic flow between various structures evolved at different scales: micro, meso, and macro, respectively. Considering that micro components will end by encapsulating any stability state initiated by a perturbation of the macroscale, the most popular framework used to analyze complex networks associates in a reverse-engineering manner properties of the micro-scale components with global network characteristics. Although the principle of convergence is widely agreed upon, we consider the transition from micro to macro one not studied enough, and the highlighting of an in-between layer being necessary exemplifies the convergence mechanism better. Our work is willing to improve existing approaches of understanding the intricate beauty behind network dynamics by embracing the chaos they evolve and not order it using inflexible analytical mechanisms. We formulate a series of research questions that are willing to demonstrate that the existence of a mesoscale structure, namely network motifs, is connected with a variety of microscale, respectively, macroscale properties - hence strengthening the proof of macro–meso–micro convergence. Our approach could provide a substantial improvement in the models used to understand molecular self-assembly, protein-based cellular adaptation, social interactions, and many other fields, where connections between structures or components play an essential role. [ABSTRACT FROM AUTHOR]

Edutainment applications are a type of software that is designed to be entertaining while also being educational. In the current COVID-19 pandemic context, when children have to stay home due to the social distancing rules, edutainment applications for young children are more and more used each day. However, are these applications ready to take the place of an in-person teacher? In this paper, we propose a new generation of edutainment applications that are more suitable for preschoolers (aged 3–6 years old in our country) and closer to the in-person student–teacher interaction: emotions aware edutainment applications. We discuss the most important challenges that must be overcome in developing this kind of applications (i.e., recognizing children's emotions, enhancing the edutainment application with emotion awareness, and adapting the interaction flow) and the first steps that we have taken for developing them. [ABSTRACT FROM AUTHOR]

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given. [ABSTRACT FROM AUTHOR]

The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on simple, local interactions. We show that, in the context of image segmentation, a butterfly effect arises when we perturb the neighbourhood system of a cellular automaton. Specifically, we enhance a classical GrowCut cellular automaton with chaotic features, which are also able to improve its performance (e.g., a Dice coefficient of 71% in case of 2D images). This enhanced GrowCut flavor (referred to as Band-Based GrowCut) uses an extended, stochastic neighbourhood, in which randomly-selected remote neighbours reinforce the standard local ones. We demonstrate the presence of the butterfly effect and an increase in segmentation performance by numerical experiments performed on synthetic and natural images. Thus, our results suggest that, by having small changes in the initial conditions of the performed task, we can induce major changes in the final outcome of the segmentation. [ABSTRACT FROM AUTHOR]

We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort. [ABSTRACT FROM AUTHOR]

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level. [ABSTRACT FROM AUTHOR]